class_docstr: A 3x3 convolution layer with some utility functions.
class_docstr_tok: ["A","3x3","convolution","layer","with","some","utility","functions","."]
code: def load_waifu2x_json(self, layer: dict):
      # Weights in this file are outChannel,inChannel,X,Y.
      # Not outChannel,inChannel,Y,X.
      # Therefore, transpose it before assignment.
      # I have long since forgotten how I worked this out.
      self.W.assign(
          Tensor(layer["weight"])
          .reshape(shape=self.W.shape)
          .transpose(2, 3)
      )
      self.b.assign(Tensor(layer["bias"]).reshape(shape=self.b.shape))
  
file_path: tinygrad/examples/vgg7_helpers/waifu2x.py
imports: import numpy
  from tinygrad.tensor import Tensor
  from PIL import Image
  from tinygrad.helpers import fetch
label_desc: Use image node_id 4 for calling the Conv3x3Biased obj's underlying member method code with example usage: obj.load_waifu2x_json(layer) without return types